Systems, methods, and devices for brain stimulation and monitoring

ABSTRACT

Provided are systems, methods, and devices for brain stimulation and monitoring. Brain stimulation may be provided to enhance slow wave and spindle synchrony. Such stimulation may provide increased memory consolidation. Furthermore, brain stimulation modalities may provide enhanced sleep and awakening. For example, transcranial direct current stimulation of the brain stimulation may enhance sleep quality. Moreover, one or more markers characterizing unconsciousness are may be identified based on changes in measured power densities or spectra. Further still, the above described modalities of brain stimulation may be implemented in an open-loop or closed loop manner. Brain state parameters may be generated for building models of the brain based on determined synchrony patterns between slow waves and spindles. The models may be used to determine a mediation procedure for adjusting the intensity of slow wave oscillations to enhance slow wave and spindle synchrony.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority under 35 U.S.C. § 119(e) to U.S.Provisional Application No. 62/579,289, filed Oct. 31, 2017, entitledSYSTEMS, METHODS, AND DEVICES FOR BRAIN STIMULATION AND MONITORING, thecontents of each of which are hereby incorporated by reference.

TECHNICAL FIELD

The present disclosure relates to mechanisms and processes directed tostimulation and monitoring associated with brain activity.

DESCRIPTION OF RELATED ART

A human brain may include neurons which exhibit measurable electricalsignals when active. Accordingly, various measuring modalities, such aselectrodes, may be used to measure such electrical activity. The neuralactivity of neurons may include many a variety of frequency components.Accordingly, such electrical activity may be measured and represented asa power spectrum in a frequency domain. Moreover, different behaviors orpatterns in different frequency bands may be identified and referred toas particular types of waves, such as delta and theta waves.

SUMMARY

Provided are systems, methods, and devices for closed loop controlassociated with brain activity.

In various embodiments, systems, methods, and devices are provided forbrain stimulation and monitoring. Brain stimulation may be provided toenhance slow wave and spindle synchrony. Such stimulation may provideincreased memory consolidation. Furthermore, brain stimulationmodalities may provide enhanced sleep and awakening. For example,transcranial direct current stimulation of the brain stimulation mayenhance sleep quality. Moreover, one or more markers characterizingunconsciousness are may be identified based on changes in measured powerdensities or spectra. Further still, the above described modalities ofbrain stimulation may be implemented in an open-loop or closed loopmanner.

In one aspect, methods may comprise obtaining a plurality ofmeasurements from a brain of a user via an interface. The plurality ofmeasurements may include indications of slow wave oscillations and sleepspindles. The methods may further comprise generating, via a firstprocessing device comprising one or more processors, a plurality ofbrain state parameters characterizing one or more features of at leastone brain state of the brain of the user. The brain state parameters mayinclude an indication of a synchrony pattern between the measured slowwave oscillations and the measured sleep spindles.

The methods may further comprise, via a second processing devicecomprising one or more processors, generating a model of the brain ofthe user based, at least in part, on the plurality of measurements andthe synchrony pattern; and determining, using the model of the brain ofthe user and training data comprising one or more mediation data points,a procedure for mediation configured to adjust the intensity of slowwave oscillations.

The methods may further comprise generating one or more control signals,via a controller, based on the procedure for mediation. The one or morecontrol signals are transmitted to the interface.

The methods may further comprise generating, via the interface, one ormore stimuli based on the one or more control signals. The one or morestimuli may be configured to increase slow wave oscillation power of thebrain. The methods may further comprise applying the one or more stimulito cortical tissue of the brain.

In some embodiments, the one or more stimuli is configured to enhanceslow wave oscillation and spindle synchrony. In some embodiments, the atleast one brain state includes an unconscious state corresponding tonon-rapid-eye-movement sleep.

In some embodiments, generating a plurality of brain state parametersincludes assessing a temporal directionality of interactions between themeasured slow wave oscillations and sleep spindles.

In some embodiments, the methods may further comprise providing theprocedure for mediation to one or more entities. Such one or moreentities includes a client device corresponding to a medicalprofessional.

Other implementations of this disclosure include corresponding devices,systems, and computer programs configured to perform the describedmethods. These other implementations may each optionally include one ormore of the following features. For instance, provided systems maycomprise an interface configured to obtain a plurality of measurementsfrom a brain of a user. The plurality of measurements may includeindications of slow wave oscillations and sleep spindles.

The systems may further comprise a first processing device comprisingone or more processors configured to generate a plurality of brain stateparameters characterizing one or more features of at least one brainstate of the brain of the user. The brain state parameters may includean indication of a synchrony pattern between the measured slow waveoscillations and the measured sleep spindles.

The systems may further comprise a second processing device comprisingone or more processors configured to: generate a model of the brain ofthe user based, at least in part, on the plurality of measurements andthe synchrony pattern; and determine, using the model of the brain ofthe user and training data comprising one or more mediation data points,a procedure for mediation configured to adjust the intensity of slowwave oscillations. The systems may further comprise a controllercomprising one or more processors configured to generate one or morecontrol signals based on the procedure for mediation. The one or morecontrol signals are transmitted to the interface.

This and other embodiments are described further below with reference tothe figures.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

FIG. 1A illustrates an example of a system for monitoring andstimulating brain activity, configured in accordance with someembodiments.

FIG. 1B illustrates an example of standard electroencephalogramelectrode array which may be implemented with various embodiments.

FIGS. 2A-2D illustrate memory task and oscillatory signatures of sleep,in accordance with some embodiments.

FIGS. 3A-3G illustrate SO-Spindle interactions in old and young adults,in accordance with some embodiments.

FIGS. 4A-4D illustrate timing of SO-spindle interactions predictingmemory retention, in accordance with some embodiments.

FIGS. 5A-5B illustrate dependency of directional SO-spindle coupling onprefrontal grey matter volume, in accordance with some embodiments.

FIGS. 6-11 illustrate brain stimulation used to enhance sleep andfacilitate awakening, in accordance with some embodiments.

FIG. 12 illustrates a flowchart of an example of a method for providingbrain monitoring and stimulation, implemented in accordance with someembodiments.

FIG. 13 illustrates an example of a computer system capable ofimplementing various processes described in the present disclosure.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

Reference will now be made in detail to some specific examples of theinvention including the best modes contemplated by the inventors forcarrying out the invention. Examples of these specific embodiments areillustrated in the accompanying drawings. While the present disclosureis described in conjunction with these specific embodiments, it will beunderstood that it is not intended to limit the invention to thedescribed embodiments. On the contrary, it is intended to coveralternatives, modifications, and equivalents as may be included withinthe spirit and scope of the invention as defined by the appended claims.In addition, although many of the components and processes are describedbelow in the singular for convenience, it will be appreciated by one ofskill in the art that multiple components and repeated processes canalso be used to practice the techniques of the present disclosure.

In the following description, numerous specific details are set forth inorder to provide a thorough understanding of the present invention.Particular embodiments of the present invention may be implementedwithout some or all of these specific details. In other instances, wellknown process operations have not been described in detail in order notto unnecessarily obscure the present invention.

According to embodiments disclosed herein, brain stimulation may beprovided to enhance slow wave and spindle synchrony. As will bediscussed in greater detail below, such stimulation may provideincreased memory consolidation. More specifically, such stimulation maybe provided to enhance slow wave, or delta bursts, and spindle synchronyduring a state of sleep. Increases in such synchrony during sleep mayenhance memory consolidation that normally occurs during sleep.Accordingly, embodiments disclosed herein relate to delta burst couplingwith spindles, and how this is disrupted in aging.

Furthermore, according to embodiments described herein, brainstimulation modalities are disclosed for enhanced sleep and awakening.For example, transcranial direct current stimulation of the brainstimulation may enhance sleep quality. More specifically, when suchstimulation is provided pre-sleep, the quality of sleep may be improved.In this way, embodiments disclosed herein provide stim enhanced sleepand faster awakening.

In embodiments disclosed herein, one or more markers characterizingunconsciousness are disclosed. Such a marker may be a universal marker.This marker may be used to identify unconsciousness when a subject orpatient is either asleep or under anesthesia. In various embodiments,the marker may be identified based on a change in the 1/f slope forpower between 30-60 Hz. Accordingly, changes in measured power densitiesor spectra may be utilized to identify a marker or parameter thatcharacterizes a state of unconsciousness. Such techniques may be usedfor other types of monitoring as well.

In various embodiments, the above described modalities of brainstimulation may be implemented in an open-loop or closed loop manner.Accordingly, brain stimulation may be provided such that stimulation isprovided independent of an output, and according to a designatedstimulation protocol. In various embodiments, brain stimulation may beprovided in a closed-loop manner in which stimulation is provided andmodified based on measurements and changes in a subject's neuralactivity. Accordingly, the above-mentioned stimulation modalities may bemodified and refined based on neural and behavioral measurements of asubject.

In various embodiments, the coupled interaction between slow waveoscillations and sleep spindles during non-rapid-eye-movement (NREM)sleep has been proposed to support memory consolidation. However, littleevidence in humans supports this theory. Moreover, whether such dynamiccoupling is impaired as a consequence of brain aging in later life,contributing to cognitive and memory decline, is unknown. Combining,electroencephalography (EEG), structural MRI and sleep-dependent memoryassessment, these questions were addressed in cognitively normal youngand older adults. Directional cross-frequency coupling analysesdemonstrated that the slow wave governs a precise temporal coordinationof sleep spindles, the quality of which predicts overnight memoryretention. Moreover, selective atrophy within the medial frontal cortexin older adults predicted a temporal dispersion of this slowwave-spindle coupling, impairing overnight memory consolidation leadingto forgetting. Prefrontal dependent deficits in the spatiotemporalcoordination of NREM sleep oscillations provide a novel pathway toage-related memory decline.

The precise temporal coordination of NREM sleep oscillations has beenproposed to support the long-term consolidation of memory. Within thesetheoretical frameworks, temporal interactions between cortical slowoscillations (“SO”; <1.25 Hz), sleep spindles (˜12-16 Hz), andhippocampal ripples (˜80-100 Hz) form a hierarchy that allows forinformation transformation necessary for long-term memory retention. Inparticular, the depolarizing ‘up-states’ of the SO are proposed tofacilitate sleep spindle and ripple expression, with hippocampal ripplesbeing temporally nested into spindle troughs. The coupling of these NREMoscillations is thought to support intrinsically timed informationtransfer across several spatiotemporal scales underlying long-termmemory.

There is, however, limited empirical evidence supporting thisoscillatory interaction model of hippocampal memory consolidation.Non-invasive brain stimulation findings have demonstrated that boostingSO power can indirectly co-modulate sleep spindle activity, whileSO-spindle coupling during a nap in young adults tracks offline memoryretention. Yet the mechanistic relationship of SO-spindle synchrony, andhow this determines the success or failure of overnighthippocampal-dependent memory consolidation remains unknown, as does thecausal necessity of brain regions in supporting coupled NREM oscillationdynamics and memory benefit.

Regarding the latter, there is growing evidence that aging markedlydisrupts sleep and overnight memory consolidation. If sleep oscillatorycoupling is compromised in older adults, what is it about the agingbrain that degrades interactive synchrony of NREM oscillations leadingto memory impairment? This question is of special relevance as it mayreveal a currently under-appreciated mechanism (impaired SO-spindlecoupling) that contributes to cognitive memory decline in later life,and if identified, would defined a novel therapeutic target for clinicalintervention.

These unanswered questions were addressed by combining structural MRI,polysomnography with full-head (19 channel) scalp electroencephalography(EEG), and the assessment of sleep-dependent hippocampal memory, inyoung and older adults. Specifically tested was the hypothesis that theprecise temporal coupling of cortical NREM SO-spindles, as predicted bytheoretical models, facilitates overnight memory retention in youngadults, and whether older adults have a temporal un-coupling of theseoscillations leading to impaired overnight memory. Moreover, based onevidence in young and older adults demonstrating that the structuralgrey-matter morphology of the medial prefrontal cortex (mPFC) isassociated with the quality of SO, and that this same mPFC region is anEEG source generator of SO linked to spindles, the hypothesis thatstructural grey matter integrity of mPFC predicts the degree ofcompromised SO-spindle dynamic coupling in older adults was furthertested.

With reference to FIG. 1A, shown is an example of a system 100 formonitoring and stimulating brain activity, configured in accordance withsome embodiments. In various embodiments, system 100 may be implementedfor providing closed loop or open loop control in treatments andcognitive enhancements. In some embodiments, system 100 includes aninterface, such as interface 102. In various embodiments, interface 102is a brain interface that is configured to be coupled with a brain, suchas brain 101. As will be discussed in greater detail below, suchcoupling may provide bidirectional communication, or may be used forvarious sensing modalities. In some embodiments, interface 102 includesvarious electrodes, as may be included in an electrode array. Suchelectrodes may be included in a scalp potential electroencephalogram(EEG) array, may be deep brain stimulation (DBS) electrodes, or may bean epidural grid of electrodes. In other examples, interface 102 mayinclude optogenetics mechanisms for monitoring various neuronalprocesses. Mechanisms may be used to make various measurements andacquire measurement signals corresponding to neural activity. As usedherein, neural activity may refer to spiking or non-spikingactivity/potentiation.

In various embodiments, such measured signals may be electrical signalsderived based on neural activity that may occur in cortical tissue of abrain. Such measurements may be acquired and represented in a timedomain and/or frequency domain. In this way, neural activity may bemonitored and measured over one or more temporal windows, and suchmeasurements may be stored and utilized by system 100. In variousembodiments, such neural activity may be observed for particular regionsof cortical tissue determined, at least in part, based on aconfiguration of interface 102. In one example, this may be determinedbased on a configuration and location of electrodes included ininterface 102 and coupled with the brain.

FIG. 1B illustrates an example of standard electroencephalogram (EEG)electrode array 150 which may be implemented with various embodiments.FIG. 1B depicts standard EEG electrode names and positions along thehead 152 of a user in vertex view with nose 154 above, left ear 156 toleft, and right ear 158 to right. In various embodiments, EEG array 150may be implemented as interface 102.

In various embodiments, EEG array 150 includes any number of electrodes.Such electrodes may include midline electrodes on midline Z, includingFZ, Midline Frontal; CZ, Midline Central; PZ, Midline Parietal; OZ,Midline Occipital. In various embodiments, even numbers refer to righthemisphere locations, and odd numbers refer to left hemisphere locationsincluding: Fp, Frontopolar (Fp1 and Fp2); F, Frontal (F3, F4, F7, F8);C, Central (C3, C4); T, Temporal (T7, T8); P, Parietal (P3, P4, P7, P8);O, Occipital (O1, O2). The standard 19, 10 to 20 electrodes are shown asblack points. An additional subset of five, 10-10 electrodes are shownas open circles including FT, Frontotemporal (FT9, FT10); TP,Temporoparietal (TP9, TP10), and OZ. In various embodiments, EEG array150 may include fewer or additional electrodes than shown in FIG. 1B.

According to some embodiments, one or more components of interface 102are configured to provide stimuli to the brain coupled with interface102. For example, one or more electrodes included in interface 102 maybe configured to provide electrical stimuli to cortical tissue of thebrain. As discussed above, such electrodes may be implemented utilizingone or more of various modalities which may be placed on a user's scalp,or implanted in the user's brain.

As will be discussed in greater detail below, such actuation and stimuliprovided by interface 102 may be of many different modalities. Forexample, stimuli may be aural, visual, and/or tactile as well as beingelectrical and/or magnetic, or any suitable combination of these.Accordingly, interface 102 may further include additional components,such as speakers, lights, display screens, and mechanical actuators thatare configured to provide one or more of aural, visual, and/or tactilestimuli to a user. In this way, any suitable combination of differentmodalities may be used. For example, a combination of electrical andaural stimuli may be provided via interface 102. Further still,interface 102 may include different portions corresponding to signalacquisition and stimuli administration. For example, a first portion ofinterface 102 may include electrodes configured to measure neuralactivity, while a second portion of interface 102 includes speakersconfigured to generate aural stimuli. In various embodiments, the visualand/or auditory stimuli may be provided via one or more communicationschannels that are configured to provide such stimuli. For example, suchstimuli may be provided via a streaming media service or a socialnetworking service. In one example, such stimuli may be provided via adedicated YouTube channel that is streamed to interface 102.

In some embodiments, interface 102 further includes one or morededicated processors and an associated memory configured to obtain andstore the measurements acquired at interface 102. In this way, suchmeasurements may be stored and made available to other system componentswhich may be communicatively coupled with interface 102.

System 100 further includes one or more processing devices. A firstprocessing device 104 may be configured to generate brain stateparameters that may characterize and identify features of brain statesand generate estimations of brain state signatures. In variousembodiments, a brain state may refer to one or more identified patternsof neural activity. Accordingly, such brain states may be one or moreidentified patterns, such as oscillation or fluctuation of activity at aparticular frequency band, such as low oscillatory behavior as well asdelta, theta, alpha, beta, and gamma waves. Furthermore, such brainstates may be identified based on coupling between patterns of neuralactivity. For example, a brain state may be identified based onoscillation or fluctuation of activity at a particular frequency band,and an increase of activity in another. Other brain states maycorrespond to phase resets in prefrontal and cingulate areas. Phaseresets may correspond to coherent activity in widespread corticalregions and impact timing of neuronal activity. Activity patterns in theprefrontal cortex can be monitored, identified, and controlled forassociations with particular behaviors including goal directed behavior.Neuronal synchronization and desynchronization may be detected andmanaged using closed loop control based on intelligent and continuouslyadaptive neurological models. As will be discussed in greater detailbelow, such identification may be implemented based, at least in part,on various parameters, such as observers and estimators.

Accordingly, first processing device 104 is configured to generate oneor more particular observers and/or estimators that may form the basisof identification and estimation of brain states, described in greaterdetail below. As discussed above, first processing device 104 may beconfigured to generate deterministic and stochastic observers andestimators of brain states based on acquired measurements. Suchdeterministic observers may provide robustness to exogenousdisturbances, while such stochastic estimators may provide robustnessregarding noise. For example, first processing device 104 may beconfigured to implement linear and nonlinear observation and estimationmodalities such as luenberger, kalman, sliding mode, and benes filters.The application of such observation and estimation modalities may beused to generate and infer one or more parameters associated with brainstates. For example, such parameters may identify aspects of particularbrain states, such as an oscillation or resonance frequency as well as acoupling and/or weighting factor associated with one or more other brainstates. In a specific example, a condition of Schizophrenia may bemodeled as a pair of oscillators, each being an oscillating neuralpattern, and first processing device 104 may be configured to identifyand determine resonance frequencies and coupling factors associated withthe oscillators based on the previously described acquired measurements.

Thus, according to various embodiments, first processing device 104 maybe configured to implement direct and indirect state signatureestimation. First processing device 104 may also be configured toimplement brain system model parameter identification and adaptation.Furthermore, first processing device 104 may also be configured togenerate an estimation of the stability of underlying hidden states, aswell as noise estimation and rejection in underlying measurements. Invarious embodiments, first processing device 104 may also be configuredto implement cognitive relevance based measurement window sizing.Accordingly, measurement windows may be sized based on cognitiverelevance measures, and may be sized dynamically.

In some embodiments, first processing device 104 is configured toimplement baseline estimation and removal which may enhance sensitivityregarding signatures/events. As discussed above, neural activity may bemeasured and represented in a time domain and/or a frequency domain. Inone example, when represented in a frequency domain, the neural activitymay be represented as a power spectrum that follows a plot that islogarithmic or non-linear. Accordingly, in various embodiments, firstprocessing device 104 is configured to implement one or morecurve-fitting modalities to estimate a baseline of the plot, and removesuch baseline to provide a more accurate representation of more granularfeatures of the measured signal. Such granular features may berepresented with greater accuracy, and be used to identify parameters ofbrain states with greater accuracy.

First processing device 104 may also be configured to implement learningestimator models that learn state changes and estimate them. Suchlearning estimator models may also learn changing system parameters, andestimate the improvement/retrograde of behavioral/functional responses.

As similarly discussed above, observers and estimators may be used toidentify and/or infer state signatures and parameters associated withbrain states. For example, examples of brain state signatures mayinclude certain lower frequency oscillations mediated with or coupled tohigher frequency oscillations (delta to alpha, alpha to gamma, theta tomu, alpha to high frequency band) that correspond with various levels ofcognitive ability and various types of cognitive conditions to detectand identify signatures indicative of particular types of cognitiveperformance or cognitive conditions.

According to various embodiments, slow wave coupled spindle synchronyduring sleep may be used as a signature of memory retention andconsolidation. In particular embodiments, a closed loop control systemmay be configured to monitor and improve slow wave coupled spindlesynchrony. In another example, first processing device 104 is configuredto detect multiplexed parallel delta to theta, alpha to beta,delta/theta/alpha to high-frequency-band coupling to identify asignature of working memory. In yet another example, first processingdevice 104 is configured to monitor and/or control a baseline rate ofexponential decay in a spectral composition of neural activity, as wellas deviations from the baseline to identify and predict a cognitive andarousal state, such as an inhibitory or excitatory state. In variousembodiments, first processing device 104 may also be configured tomonitor beta synchronization or desynchronization to identify asignature of motor activity intent. In this way, any number of brainstates and associated signature and parameters may be identified andestimated by first processing device 104. In some embodiments, firstprocessing device 104 may be configured to identify an “activity silent”mode associated with a user in which a measure of activity is measuredand tracked as a mental state shift indicator.

System 100 may further include second processing device 106 which isconfigured to generate functional and structural models of the braincoupled with interface 102. In various embodiments, second processingdevice 106 is further configured to provide brain functional modelidentification and adaptive learning, as well as brain structural modelswith adaptive learning. In various embodiments, second processing device106 is communicatively coupled with interface 102, first processingdevice 104, as well as controller 108 discussed in greater detail below.Accordingly, second processing device 106 may receive input signals fromone or more other system components and utilize such inputs to formand/or update functional and structural models of the brain coupled withinterface 102. In various embodiments, second processing device 106 mayalso be configured to pre-process inputs received from interface 102 andfirst processing device 104 to generate one or more composite inputs.

In various embodiments, second processing device 106 is configured togenerate functional input-output univariate and multivariate models thatmay be configured to approximate at least some of the input-outputbehavior of the brain coupled with interface 102 described above. Insome embodiments, second processing device 106 is also configured toimplement adaptive learning brain models that may iteratively update andimprove the functional model of the brain that has been constructed.

In some embodiments, second processing device 106 is configured toimplement deep/machine learning and data mining based system models.Accordingly, second processing device 106 may be configured to implementone or more artificial neural networks that may be configured to modeltasks or cognitive functions of the brain. Such neural networks may beimplemented in a hierarchical manner. Moreover, such neural networks maybe trained based on signals received from other components of system100. For example, models may be trained based on inputs provided tointerface 102 from controller 108 and outputs measured via interface 102as well as observers and estimators generated by first processing device104.

In this way, input and output activity within system 100 may be used, atleast in part, to construct functional and structural models representedby second processing device 106. More specifically, the artificialneural networks created by second processing device 106 may be modifiedand configured based on activity of the human brain. In this way, theartificial neural networks created by second processing device 106 maybe specifically configured based on behaviors and processing patterns ofthe human mind.

In some embodiments, second processing device 106 is further configuredto construct artificial neural networks that alter one or moreparameters of treatment or administration of brain stimulation on anindividual, based on one or more received inputs. In some embodiments,such parameters of treatment may include the schedule or nature foradministration of brain stimulation treatment, as well as the timeframe, duration, frequency, and/or strength of treatment following abrain injury. In some embodiments, the one or more received inputs maybe received information regarding the efficacy or likelihood of successor mitigation of symptoms of a brain injury in relation to the timeframe of administration of brain stimulation treatment. In someembodiments, the artificial neural networks are modified or adjustedbased on updated datasets or additional input information correspondingto the timing of treatments and the results of treatments. For example,such input information may be training data that is utilized by neuralnetworks upon which one or more machine learning algorithms areexecuted, such that the neural networks learn improved techniques forhow to more effectively administer brain stimulation treatments,including adjusted timing, duration, frequency, and/or nature oftreatments.

In various embodiments, second processing device 106 is furtherconfigured to implement system identification, dimensional modeling, anddimension reduction to identifying preferred models. Furthermore, secondprocessing device 106 may be configured to implement identification ofone or more brain states to be controlled, as may be determined based onactuation sensitivity and efficacy. In various embodiments, secondprocessing device 106 is configured to implement identification of themost sensitive brain states to be measured. Such identification mayidentify the most sensitive and granular measurement that identifies thebrain state changes. In some embodiments, second processing device 106is configured to implement multi-input, multi-output measurement andactuation. In some embodiments, second processing device 106 is furtherconfigured to multi-time scale modelling (to capture the slow system andfast system dynamic accurately). In various embodiments, secondprocessing device 106 is also configured to implement one or more neuralnet basis functions that may be configured and or generated based onactivity of the brain. For example, such functions may include spikefunctions, multi-input-coevolution triggered firing (such as coherence,synchrony, coupling, correlation of two waveforms triggering a cellfiring).

In some embodiments, second processing device 106 is further configuredto capture or record results of treatments and various parameters andinformation related to treatments, including pieces of informationrelating to the efficacy of treatments and/or the mitigation or lack ofmitigation of one or more symptoms of an injury. In some embodiments,second processing device 106 is also configured to capture or record thetime frames of various treatments of individuals. In some embodiments,time frames include the timing, duration, frequency, and/or nature oftreatments following the event of an injury to the brain. In someembodiments, second processing device 106 is configured to retrieveexisting or historical information or datasets relating to treatments,such as information relating to the efficacy of treatments or time frameof treatments, including timing, duration, frequency, and or nature oftreatments following the event of an injury to the brain.

While first processing device 104 and second processing device 106 havebeen described separately, in various embodiments, both first processingdevice 104 and second processing device 106 are implemented in a singleprocessing device. Accordingly, a single processing device may bespecifically configured to implement first processing device 104 andsecond processing device 106.

System 100 further includes controller 108 configured to implement andcontrol closed loop control of treatments and cognitive enhancements. Invarious embodiments, controller 108 is communicatively coupled withinterface 102, first processing device 104, and second processing device106. Accordingly, controller 108 is configured to receive inputs fromvarious other system components, and generate outputs based, at least inpart on such inputs. As will be discussed in greater detail below, suchoutputs may be used to provide actuations to the brain coupled withinterface 102. For example, outputs generated by controller 108 may beused to stimulate the brain via one or more components of interface 102.In this way, controller 108 may provide stimuli to the brain viainterface 102, may receive measurements, parameter information, andmodel information via other components such as first processing device104 and second processing device 106, and may generate updated stimulibased on such received information.

For example, outputs generated by controller 108 may be used to applyvarious stimuli via interface 102 to increase the strength or frequencyof slow wave oscillations during NREM sleep in order to restore orgenerate phase coupling between slow wave oscillations and sleepspindles to enhance sleep-dependent hippocampus-dependent memoryconsolidation.

Thus, according to some embodiments, controller 108 is configured toimplement multi-input, multi-output feedback control. Controller 108 mayalso be configured to implement loop shaping optimized feedback control.In a specific example, controller 108 is configured to implement modelreference adaptive control. Furthermore, controller 108 may beconfigured to implement cognitive enhancement trajectory control. Invarious embodiments, controller 108 is configured to implement enhancedcontrol in which one or more parameters of a treatment may be enhancedby increasing its efficiency and/or effect. For example, an input orstimulation may be reduced to implement a same enhancement, a durationof stimulations may be reduced but still reach desired improvement, anda path of recovery may be made more efficient. In this way, an amount ofstimulation, which may be a combination of amplitude and duration, maybe reduced while still obtaining a desired effect, thus increasing theefficacy of the treatment and reducing overstimulation. In variousembodiments, controller 108 is configured to implement a geneticalgorithm to identify a particular stimulation pathway that reduces anamount of stimulation.

In some embodiments, controller 108 is configured to implement combinedcontrol of pharmacological and stimulation inputs. Accordingly,controller 108 may be configured to modify stimulation inputs based onan expected effect of one or more pharmacological agents that may beadministered in conjunction with the stimulation. In this way,controller 108 may modify and control administration of stimuli viainterface 102 based on an identified pharmacological regimen. In variousembodiments, controller 108 is configured to implement game theoreticstrategy-based treatments. In some embodiments, controller 108 isconfigured to implement real time measurement/estimation and control.

In various embodiments, controller 108 is configured to receive areference signal which may be used, at least in part, to generate ormodify the stimuli provided via interface 102. In various embodiments,the reference signal may be a previously determined signal or patternthat may represent a reference level or pattern of neural activity. Insome embodiments, the reference signal may be generated based on a“negative” model. In a specific example, such a negative model may be afunctional and/or structural model that is generated based on a reverseor inversion of one or more of the models created and stored by secondprocessing device 106. Accordingly, such a negative model may begenerated by second processing device 106, and the reference signal maybe generated by second processing device 106 and received at controller108.

As will be discussed in greater detail below, the above-describedcomponents of system 100 may be specifically configured to provide oneor more particular applications. For example, system 100 may beconfigured to provide bio-signal interpretation for status monitoringand diagnostics. Accordingly, the brain state information observed andderived by at least first processing device 104 and second processingdevice 106 may be used to identify brain states, the onset of particularbrain states, and particular transitions between brain states. Suchevents may be detected, and notifications may be generated by, forexample, first processing device 104, second processing device 106, orcontroller 108. Such notifications may be provided to other entities, orclient devices 110, that may be involved in status monitoring anddiagnostics, such as computing and mobile devices associated withmedical professionals, or a user's in-home medical system.

In various implementations, client device 110 may be any one of variouscomputing devices such as laptop or desktop computers, smartphones,personal digital assistants, portable media players, tablet computers,or other appropriate computing devices that can be used to communicateover a global or local network, such as the Internet. In variousembodiments, client device 110 may be configured to communicate with thefirst processing device 104, second processing device 106, and/orcontroller 108.

In some embodiments, client device 110 may receive information from thevarious other components, such as device status, performance or functioninformation, predictive models, brain state parameters, diagnosticinformation, mediation procedures, etc. For example, the brain stateparameters generated by first processing device 104 may be transmittedto client device 110. In some embodiments, functional and structuralmodels of the brain generated by second processing device 106 may betransmitted to client device. As another example, particular controlsignals may be communicated to controller 108 form client device 110 tobe sent to interface 102 or prosthetic 112. In some embodiments, clientdevice 110 may also be configured to directly communicate with interface102 or prosthetic 112, for example to transmit control signals ormonitor device status.

Moreover, system 100 may be configured to provide augmented reality(AR)-virtual reality (VR) for cognitive enhancements and side-effectremoval. For example, brain state and associated parameter detection maybe used to identify the onset of particular cognitive states, such asmotion sickness. When detected, or even when the onset is detected,controller 108 may generate one or more stimuli, which may be visualstimuli, tactile stimuli, and/or electric stimuli, to alleviate thedetected cognitive state. In this way, motion sickness associated withVR may be alleviated. In various embodiments, brain stimulation may beused to enhance sensory perception associated with AR and VR.Accordingly, one or more stimuli may be provided via interface 102 wheresuch stimuli are determined based on the AR or VR program, and suchstimuli may enhance or improve the experience of the AR or VR program.

In various embodiments, system 100 is configured to provide cognitiveand behavioral modulation specific to a particular psychological orneurophysiological condition. For example, a condition such asdepression may be characterized by a frozen brain state, or a brainstate that does not oscillate as a normal brain would. In variousembodiments, system 100 may be configured to identify a particular brainstate, identify that it is “frozen” (has not changed over a designatedperiod of time), and generate one or more control signals that areconfigured to stimulate the user's brain and change the brain state ofthe user by virtue of the stimulation to alleviate the depression. Inanother example, such generation of control signals may be used toalleviate or manage pain, as may be applicable with chronic pain. Suchcontrol signals associated with pain management may be implemented usingmulti-modal (multi-sensory) stimulation. In yet another example,generation of control signals may be utilized for stimulation basedreversal of ‘minimally-conscious’ or ‘comatose’ brain states of users.In an additional example, such control signals may be used to mitigateor alleviate age based cognitive decline. Accordingly, stimuli may beprovided via interface 102 to stimulate areas having diminished activitydue to the process of aging.

In some embodiments, system 100 may be configured to provide system andpathology specific functional brain models, as may be utilized inpharmacological applications. Accordingly, as discussed above, the useof pharmacological agents may be identified, and models may be updatedin response to such pharmacological agents being identified. In thisway, administration of stimuli via interface 102 may be modified andupdated based on the application of a pharmacological agent to a user.More specifically, such modifications may be implemented based on theidentification of the use of pharmacological agents, as well as directlymeasured neural activity of the user during the treatment process.

In another example, system 100 may be configured to provide model basedadaptive control paradigms for feedback treatments and cognitiveenhancements. Accordingly, as discussed above, treatments and cognitiveenhancements may be provided with adaptive closed loop control thatenables the modification and updating of such treatments and cognitiveenhancements based on directly measured neural activity over the courseof the treatments and cognitive enhancements.

In various embodiments, system 100 may be configured to provide smartembedded prosthetics (e.g. speech decoder, motor control). Accordingly,signals generated by controller 108 may be used to control one or moreembedded prosthetics, such as prosthetic 112. In some embodiments,prosthetic 112 may be an implanted stimulator that may be activated bycontroller 108 in response to the detection or identification of one ormore brain states or parameters associated with such brain states. Insome embodiments, prosthetic 112 may be a component of interface 102 orcommunicatively coupled to interface 102.

In a specific example, prosthetic 112 may be a stimulator configured toincrease the strength or frequency of slow wave oscillations in themedial prefrontal cortex (mPFC). In another example, prosthetic 112 maybe a stimulator configured to prevent epileptic episodes. In thisexample, controller 108 may identify a brain state corresponding to anonset of an epileptic seizure, and may provide a signal to prosthetic112 that activates prosthetic 112 to prevent the seizure. For example,prosthetic 112 may comprise a multi-channel closed-loopneural-prosthetic system-on-chip (SoC) configured for real-timeintracranial electroencephalogram (iEEG) acquisition, seizure detection,and electrical stimulation in order to suppress epileptic seizures.

In some embodiments, system 100 may be configured to provide a toolboxconfigured to support estimation, modeling, and control. In anotherexample, system 100 may be configured to provide a bio operating system(BoS) framework for biological measurement and an actuation controlplatform. As discussed above, such an operating system may beimplemented on a variety of platforms including a mobile platform suchas mAndroid. In some embodiments, system 100 may be configured toprovide task specific cognitive enhancements, such as FAA monitoringagents, and fighter pilot related tasks.

System 100 and its respective components may be implemented in a varietyof contexts. For example, system 100 may be implemented in a clinicalsetting that may include an examination room, an operating room, or anemergency room. Moreover, system 100 may be implemented in a user's homethus providing in-home monitoring, diagnostic, and treatment.Furthermore, portions of system 100 may be implemented in a firstlocation while other portions are implemented in a second location. Forexample, interface 102 may be located at a user's home, while firstprocessing device 104, second processing device 106, and controller 108are implemented remotely, as may be the case when implemented at ahospital.

Furthermore, system 100 may be implemented across multiple users. Forexample, system 100 may include multiple interfaces that are coupledwith multiple brains. In this way, measurements may be made frommultiple users, and stimuli may be provided to multiple users. In oneexample, measurements from a first user may be used to generate andprovide stimuli to a second user. In this way, synchronization of atleast part of a brain state may be implemented across multiple users.

Other systems, devices, and methods for monitoring and stimulating brainactivity described in U.S. patent application Ser. No. 16/170,675 titledMEDIATION OF TRAUMATIC BRAIN INJURY (Attorney Docket No. STIMP003) byPradeep et al., filed on Oct. 25, 2018, which application isincorporated by reference herein in its entirety and for all purposes.

Unique non-linear directional cross-frequency coupling (CFC) analyseswere implemented, together with phase-dependent correlation measures, tocapture complex neural dynamics underlying SO-spindle synchronyrelationships. Based on theoretical accounts of oscillation-based timedmemory transfer, the hypothesis that the exact timing between SO andspindles supports memory consolidation was tested. Building on theprediction that SO orchestrate sleep-dependent memory networks,implemented methods for assessing the temporal directionality of thisSO-spindle interaction were implemented, and examined if thisdirectionality predicted memory consolidation success in young and olderadults. Finally, it was tested whether regional gray matter atrophywithin the mPFC, relative to other control regions, provided astructural correlate associated with the age-related degradation ofSO-spindle coupling and associated memory decline in older adults.

Cognitively normal older (n=32; age: 73.8±5.3; mean±SD) and young (n=20;20.4±2.0 years) participants performed a sleep-dependent episodic memorytest before and after a full night of sleep (FIG. 2A). After encoding,all participants were trained to 100% criterion before initialrecognition testing (short delay; after ˜10 min). After the short delaytest, participants underwent polysomnography in the lab and were givenan 8 h sleep period starting at their habitual bedtime. They performedthe second recognition test (long delay; after ˜10 h) the next morning.Then structural MRI data to assess gray matter (GM) intensity wereobtained. Memory retention was quantified as the difference betweenrecognition performance at the long delay minus performance at the shortdelay. Consistent with existing, overnight memory retention was impairedin older adults relative to young adults (t₄₆=−3.85, p=0.0004, d=1.19),leading to the next-step quantification of differences in NREMoscillatory dynamics that may underlie these age-associated memoryimpairments.

Oscillatory Dynamics of Sleep in Old and Young Adults:

EEG power differences between older and young adults were first assessedby means of cluster-based permutation tests across all frequencies andchannels during NREM sleep (FIG. 2B; with all figures displaying datafrom electrode Cz due to the spatial distribution of SO and spindlepower, unless stated otherwise). Oscillatory power was significantlylower in older adults from 0.5 to 8.5 Hz (p=0.0020, d=1.71) as well asbetween 10.5 and 15 Hz (p=0.0080, d=1.28) in all recorded channels (FIG.2B).

Next, SO (0.16-1.25 Hz) and sleep spindle (12-16 Hz) events weredetected based on established algorithms. Analysis of inter-spindleintervals indicated that sleep spindles exhibited a non-Poisson likebehavior and were preferentially separated by 1.13-2.78 second (s)during NREM sleep, which is in accordance with the idea that <1 Hz SOcontrols sleep spindle timing and separates them by at least 1-3 cycles.

Detection of SO and sleep spindle events reliably tracked spectral sleepsignatures over a full night of sleep (FIGS. 2C and 2D for exemplaryold/young subjects; numbers of detected events are superimposed inwhite). For every participant, the SO phase during the peak of thedetected sleep spindle events was determined. Significant non-uniformcircular distributions were identified in 29/32 old adults and in 20/20young adults. Of note, differences in oscillatory power can distortcross-frequency coupling estimates. This issue was addressed byz-normalizing individual events in the time domain to alleviateamplitude differences prior to all subsequent analyses (FIG. 3A). Notethat this normalization avoids spurious coupling that has been recentlypointed out as a potential confound in cross frequency analysis. Tofurther address this concern, a validated stratification approach wasalso employed, confirming the main findings.

Additional details are now provided regarding FIGS. 2A-2D which relateto memory task and oscillatory signatures of sleep. FIG. 2A illustratesepisodic word-pair task 200. Participants learned 120 word-nonsense wordpairs. Nonsense words were 6-14 letters in length, derived from groupsof common phonemes. During encoding trials 202 (upper left) word pairswere presented for 5 s. Participants completed the criterion training204 (upper right) directly after encoding and received feedback afterevery trial. Recognition trials 206 (lower panel) were performed after ashort delay (10 min, 45 trials) and again after a full night of sleep(10 h, 135 trials). FIG. 2B illustrates EEG power spectra 210 duringNREM sleep at electrode Cz for older (blue) and young (red) adults(mean±SEM). Grey shaded areas indicate significant differences in lowand sleep spindle frequency ranges. Inset 212 depicts topographicaldistribution of SO (<1.5 Hz; upper topographies) and inset 214 depictssleep spindle (12-16 Hz; topographies on the right) power. Note thatolder subjects exhibited significantly reduced oscillatory power acrossthe whole head. Regarding FIG. 2C, in the upper left: Hypnogram 220(MT=movement time) from one exemplary of older subject and full nightmulti-taper spectrogram at Pz (lower left) with superimposed number ofdetected SO and sleep spindle events (white solid lines; 5 minaverages). Upper right: Normalized circular histogram 222 of detectedspindle events relative to the SO phase for older subjects. Note thepeak in the right lower quadrant. Lower right: Graph 224 showsPeak-locked sleep spindle average across all detected events in NREMsleep (black). Low-pass filtered events (red) highlight that the sleepspindles preferentially peaked prior to the SO ‘up-state’. FIG. 2Dillustrates an example of a young subject. Same conventions as in FIG.2C. Upper left: Hypnogram 230 from one exemplary of young adult subjectand full night multi-taper spectrogram at Pz (lower left) withsuperimposed number of detected SO and sleep spindle events (white solidlines; 5 min averages). Upper right: Normalized circular histogram 234of detected spindle events relative to the SO phase for young adultsubjects. Lower right: Graph 234 shows Peak-locked sleep spindle averageacross all detected events in NREM sleep (black). Note, that low-passfiltered events (red) highlight that the sleep spindle amplitude isincreased after the SO peak.

Aging Affects Prefrontal SO-Spindle Coupling:

Following normalization, SO trough-locked time-frequency spectrogramswere first calculated separately for older and young adults and thencompared using a cluster-based permutation approach. Multiplesignificant clusters were observed in the sleep spindle range (FIG. 3B;p=0.0160, d=1.73). Interleaved patterning in the spindle range (dashedbox, FIG. 3B) demonstrated that the timing of sleep spindles relative tothe SO was different between older and young adults. Specifically,spectrograms illustrated that sleep spindles peaked before, rather thanin time with, the SO peak in older relative to young adults (inset 326in FIG. 3C).

Mean sleep spindle activity was nested just after the SO peak in youngadults, but was misaligned in older adults, occurring earlier in therising flank of the SO (see FIGS. 3B and 3C). Significant non-uniformdistributions were present for both older (Rayleigh z=23.24, p<0.0001)and young adults (Rayleigh z=18.55, p<0.0001). However, the meancoupling direction differed significantly between groups (FIG. 3D; olderadults: −46.3°±31.2′; young adults: 3.6°±15.5°; circular mean±SD;Watson-Williams test: F1, 50=41.34; p<0.0001; η2=0.44). That is,spindles in young adults were maximal just after the SO peak, whilesleep spindles in older adults were misaligned, prematurely peakingearlier on the rising phase in the SO cycle. This effect was notconfounded by differences in spindle onset phase angles or differencesin spindle duration.

Next, differences in coupling strength between groups were assessedusing two complimentary analyses: 1) an event-locked coupling approachthat extracted the resultant vector length per subject for allSO-spindle events at every electrode, and 2) a data-driven approachbased on the modulation index and screening of a wide-range ofphase-amplitude pairs.

For the first analysis, significant cluster difference in frontaltopography was identified for older and young adults (p=0.0120, d=0.76),indicating that SO-spindle coupling was most impaired overfronto-central sensors (FIG. 3E). The second, data-driven analysis,confirmed that this fronto-central cluster effect was specific to theSO-range between the 0.5-2 Hz and the 12-16 Hz range (p=0.0150, d=0.92),indicating that stronger coupling in young adults was limited to theSO-spindle range (FIG. 3F). Both approaches were highly correlated(rho=0.7645, p<0.0001) and effects were not simply driven by differencesin the number of oscillatory events.

Cortical Slow Oscillations Coordinate Spindle Activity:

Having established differences in SO-spindle coupling between young andolder adults, and building on our hypothesis and past theoretical modelsof SO driving spindle coordination, investigated directional influencesbetween SO and sleep spindles by means of the phase slope index (PSI)was investigated next.

A cluster-based permutation test revealed that the directional influenceof SO on sleep spindle activity was impaired in older as compared toyoung adults over frontal and parieto-occipital regions (FIG. 3G;p=0.0010, d=0.81). However, while parieto-occipital directional CFC wasmarkedly reduced in older adults, it was still above zero. Thisdemonstrates that the parietal physiologic SO-spindle coupling waspartially intact in older adults.

To examine directionality, it was tested whether the PSI predicted howmuch time the sleep spindle deviated from the SO peak. A significantfrontal cluster was identified over fronto-central sensors, indicatingthat larger PSI values predicted a smaller deviance—that is, a sleepspindle peak closer to the ‘up-state’ in young relative to older adults(p=0.012, mean rho=−0.3367; older adults: 205.07±18.68 ms; young adults:60.24±8.26 ms; mean±SD). This PSI analysis demonstrated two findings: 1)The SO phase predicts spindle timing over frontal sensors, rather thanthe converse, as postulated by theoretical models that SO triggersspindle events, and 2) the timing precision was misaligned, sincedirectional influences were reduced in older adults relative to youngadults.

Additional details are now provided regarding FIGS. 3A-3G which relateto SO-Spindle interactions in old and young adults. FIG. 3A illustratesgraph 302 on the left: Trough-locked SO grand average for older (blue)and young (red) adults. Note the prominent differences in amplitude.FIG. 3A also illustrates graph 304 on the right: the SO amplitude wasnormalized for every subject prior to all other analyses to alleviatespurious effects, which could be the result of prominent power andsignal-to-noise differences. FIG. 3B illustrates a statistical map 312of SO-locked power differences across time between older and youngsubjects. Note the interleaved patterning in the sleep spindle range(12-16 Hz; dashed box). As reference, the mean SO is superimposed (blackline; rescaled). FIG. 3C illustrates graph 322 on the left: Peak-lockedspindle grand-averages for older adults (blue) with superimposedlow-pass filtered signal (black). FIG. 3C also illustrates graph 324 onthe right: Peak-locked sleep spindle grand-average for young adults(red) with superimposed low-pass filtered signal (black). FIG. 3C alsoillustrates inset 326 at the top: Averaging mean coupling phase and SDon schematic SO (cosine).

FIG. 3D illustrates graphs 332 (older adults) and 336 (young adults) inthe upper portion: The red line indicates the mean SO phase where sleepspindle power peaks. Red dots depict individual subjects. Note sleepspindle power in older adults peaks prior to the SO positive peak (0°),while sleep spindle power in young subjects peaks around 0°. FIG. 3Dfurther illustrates graphs 334 (older adults) and 338 (young adults)indicating the grand-average normalized spindle amplitude binnedrelative to the SO phase. Again, note the non-uniform distribution,which peaks around 0° for young adults, but earlier for older adults.

FIG. 3E illustrates in the upper: SO-spindle coupling strength(resultant vector length) topography 342 for older adults (left) andSO-spindle coupling strength topography 344 for young adults (right).The lower portion of FIG. 3E illustrates a statistical difference map346, which indicates that the coupling strength was significantlyreduced for fronto-central EEG sensors, while parieto-occipitalestimates did not differ (each star or asterisk denotescluster-corrected two-sided p<0.05). FIG. 3F illustrates a statisticalmap 352 of a data-driven comodulogram. The black-circled area 354highlights the significant difference between older and young adults,which was confined to the SO-spindle range. FIG. 3G illustratescross-frequency directionality analyses. Values above zero indicate thatSO drive sleep spindle activity. Graph 352 in the upper panel indicatesthat frontal SO drive sleep spindle activity in young but not olderadults (electrode Fz), while graph 356 in the lower panel indicates thatparieto-occipital SO predicts sleep spindle activity in both older andyoung adults (lower panel; Pz). However, this effect is pronounced foryoung adults (red). The topography 356 (center panel) depicts thespatial extent where directional SO-spindle influences are reduced inolder adults relative to young adults. Note that this effect wasindependent of the chosen window length.

SO-Spindle Coupling Predicts Overnight Memory Consolidation:

Having characterized the oscillatory dynamics of SO-spindle coupling andidentified impairments in these dynamics in older relative to youngadults, the hypothesis that these oscillatory dynamics predictedovernight memory retention success, and associated age-relateddifferences, was tested. Note that traditional linear correlationanalyses were not applicable given that phase is a circular metric.Cluster-corrected circular-linear correlation analyses were used toassess the non-linear relationship between optimal coupling phase andbehavior.

A significant positive cluster was identified over frontal regions(p=0.0010, mean rho=0.4353) peaking at electrode F3 (rho=0.5699; FIG.4A). To further delineate and visualize this non-linear relationship,the average memory retention scores were binned relative to theindividual mean coupling direction (10 bins, overlap: ±1 bin; greyshaded; FIG. 4A). The resulting distribution followed an invertedu-shape, demonstrating that the success of overnight memoryconsolidation was achieved when the spindle event occurred most proximalto the SO ‘up-state’ peak. When spindles occurred further from that‘up-state’ peak, the predictive influence on overnight memory retentionsuccess declined. Note this finding was not confounded by demographic orsleep architecture differences.

No other significant EEG clusters were identified when SO-spindlecoupling strength was correlated with the degree of overnight memoryretention across all subjects (FIG. 4B). To assure that these resultswere robust against differences in oscillation power and peak frequency,sleep spindle peak and amplitude distribution confounds (FIG. 4C) werecorrected for by detecting the individual sleep spindle peak frequencyfor every SO event. A significant positive cluster was observed(p=0.0040, mean rho=0.3790), which peaked at electrode C4 (FIG. 4D,rho=0.4705) indicating that the coupling phase robustly predictedovernight memory retention.

Importantly, this effect peaked in both older and young adults atneighboring electrodes (C4 in older adults: rho=0.5725; Cz in youngadults: rho=0.5678). This result demonstrates that, even though olderadults showed a reduction in SO-spindle coupling, and lower overnightmemory retention than young adults, the same predictive functionalrelationship between SO-spindle coupling and memory consolidationsuccess was observed in both groups.

Performance for older and young adults was binned, allowing theexpression of the quadratic fits to highlight the inverse u-shapedrelationship indicated by the circular-linear correlations (FIG. 4D).These findings confirmed that after correcting for power and peakfrequency differences, the degree of overnight memory retention successwas still predicted by the timing of the coupled relationship betweenthe SO and spindle (FIG. 4D). Therefore, memory consolidation successwas most accurately predicted by sleep spindle amplitude peaking justafter the SO ‘up-state’ peak.

Additional details are now provided regarding FIGS. 4A-4D whichillustrate how the timing of SO-spindle interactions predicts memoryretention. FIG. 4A illustrates topography 402 in the upper, which showscluster-corrected circular-linear correlation analysis between theindividual mean SO-spindle coupling phase and overnight memory retentionafter correction for power differences (* indicates significantsensors). The strongest effect was observed at electrode F3. FIG. 4Aalso illustrates graph 404 in which blue dots indicate older adults andred dots indicate young adults. The mean behavioral performance werebinned relative to the coupling phase in 10 overlapping bins tohighlight the u-shaped, non-linear relationship. FIG. 4B illustratestopography 412 and graph 414 showing no significant correlation beingobserved between coupling strength (resultant vector length) and memoryretention (same conventions as in FIG. 4A).

FIG. 4C illustrates sleep spindle frequency relative to SO cycle at afrontal electrode (left graph 424) and at a parieto-occipital electrode(Pz) (right graph 426). Frontal sleep spindles are slower than posteriorsleep spindles. Their frequency only varies as a function of the SOphase over frontal regions where it is significantly lower for olderadults as shown in topography 422 (top panel). FIG. 4D illustratestopography 432 and graph 434 showing cluster-corrected circular-linearcorrelations after correcting for differences in power distributions andsleep spindle frequencies (same conventions as in FIG. 4A). Importantly,memory retention was coupling phase dependent in older and young adults.Overall the best performance was observed when the sleep spindles peakjust after the SO peak. Blue dots depict older adults. Dark grey barsindicate mean binned memory performance for older adults; the blacksolid line 436 depicts a quadratic fit to approximate the non-linearu-shaped relationship for older adults. Red dots depict young adults.Light grey bars indicate mean binned memory performance for youngadults; and the dashed black line 438 depicts a quadratic fit toapproximate the non-linear u-shaped relationship for young adults.

Age-Related Grey Matter Atrophy Predicts Coupling Deficits:

Collectively, the above analyses established 1) the oscillatory dynamicsof SO-spindle coupling, demonstrated impairments in these dynamics inolder relative to young adults, and 2) identified that thespatiotemporal precision of SO-spindle coupling predicted the degree ofovernight memory retention success and when impaired in older adults,predicted greater overnight forgetting.

Finally, the study sought to determine a potential underlyingpathological mechanism accounting for why older adults suffer theseimpairments. The study focused a priori on mPFC grey matter, based onthe prominent role of the mPFC in SO oscillatory generation.Specifically, the hypothesis that mPFC grey matter atrophy predicts thedegree of compromised SO-spindle dynamic coupling was tested.

To rule out age-related confounds, all structural metrics by the totalintracranial volume were corrected, which were correlated (rho=−0.2919,p=0.0358). cluster-based permutation correlation analyses was thenutilized to assess whether the grey matter volume in any ROI predicteddirectional SO-spindle coupling as measured by the PSI. Consistent withthe hypothesis, grey matter volume in mPFC positively correlated withdirectional coupling (FIG. 5A; p=0.0080, mean rho=0.3321), indicatingthat as grey matter volume in mPFC decreases, directional phase couplingbetween SO and sleep spindles is weakened. Note, that the results werecomparable when corrected for total brain volume (r=0.29, p=0.0343). Inaddition, age was partialled out from the cluster-based correlation test(see Methods) and again obtained a significant frontal cluster(p=0.0490; mean rho=0.25).

While our results confirmed the key role of SO in the coupling dynamicsand associated memory consolidation benefit, sleep spindles, which aregrouped by the SO, are anatomically recognized to be thalamo-corticalmediated events. Given these findings additional post-hoc analyses wereperformed to examine whether grey matter volume in thesespindle-associated regions also predicted impairments in SO-spindlecoupling the associated memory benefit.

Grey matter volume was extracted for all regions of interest (ROI) wheresleep related oscillations are thought to emerge: hippocampus and thethalamus, in addition to the neighboring lateral orbitofrontal cortex(OFC) or dorsolateral PFC (DLPFC) and several control ROIs (occipital,precuneus, posterior cingulate cortex and posterior parietal cortex),but no significant effects were observed for these other eight ROIs(FIG. 5B). These results confirmed the key role of mPFC in alteredSO-spindle coupling—an anatomical-physiological relationship that wasnot observed for other likely candidate regions.

Together, our results provide the first demonstration that: 1) theprecisely coordinated timing between the cortical NREM SO ‘up-state’ andthe sleep spindle predicts successful hippocampus-dependent memoryconsolidation; 2) Temporal disruption of this coordinated NREMoscillation coupling in older relative to young adults predicts impairedhippocampus-dependent overnight memory consolidation; and 3) Onepathological mechanism associated with impairment in spatiotemporalcoupling of the cortical SO with sleep spindles in older adults is theseverity of mPFC gray matter atrophy.

Additional details are now provided regarding FIGS. 5A-5B whichillustrate that directional SO-spindle coupling depends on prefrontalgrey matter volume. FIG. 5A illustrates in the upper left: Definition ofthe mPFC ROI on coronal 502, sagittal 504 and axial 506 slices. FIG. 5Aalso illustrates in the upper right: Topographic map 510 ofcluster-corrected correlation analysis between grey matter (GM) volumeand the directional CFC (PSI), which revealed that directionalinfluences were stronger when subjects' had more GM volume. In the lowerpanel, FIG. 5A illustrates a scatter plot 512 of significant correlationat electrode Fz. Hence, age-related GM atrophy might lead to a breakdownof SO-mediated spindle coupling. Note that GM volume was corrected forage-related total intracranial volume. FIG. 5B illustrates arelationship that was limited to mPFC, and was not observed in otherselect regions including the hippocampus 522, thalamus 524, adjacentregions such as the OFC 520 and DLPFC 528, nor in any of additionalcontrol regions (occipital 526, precuneus 530, posterior cingulate 532and posterior parietal 534).

The Oscillatory Hierarchy of Sleep-Dependent Memory Consolidation:

A long-standing proposal in models of sleep-dependenthippocampus-dependent memory consolidation involves the timedinteractive coupling between SO and sleep spindles. Indirect evidence todate has involved demonstrating that individual properties of SO andsleep spindles are linked to successful overnight memory retention.Seminal intracranial EEG studies have further highlighted thehierarchical coupling of cortical SO, cortico-thalamic sleep spindles,and hippocampal ripples. However, no assessment of memory was performedin these studies, leaving the functional relevance of these coupled NREMoscillation relationships has remained unclear. Moreover, no directassessment of the directionality of the coupling of these events hasbeen reported.

Here, these issues are addressed using directional cross-frequencycoupling analyses, and determined how SO modulate sleep spindle timing,amplitude, and peak frequency. Our results reveal a uniquespatiotemporal profile of the coupling relationship between SO and sleepspindles in young adults, such that sleep spindle amplitude peakedaround the cortical ‘up-state’. Moreover, this precise temporalrelationship was especially pronounced over centro-parietal regions—oftopographical relevance as it may be considered the anatomicalconvergence zone between the known frontal dominance of the SO and theparietal dominance of broad-range (11-16 Hz) spindles.

A second key finding revealed by the current study is that the normallyprecise spatiotemporal coordination of the SO-spindle coupled event isimpaired in older adults. Unlike young adults, in which spindle eventsexpressed a strong coincidence with the cortical SO ‘up-state’, spindleoscillations in older adults arrived, on average, further away from thedepolarizing upswing of the SO cycle, occurring prior to it rather thanjust after the depolarization envelop. In addition, this phase couplingwas more dispersed over SO in older adults. These findings provideevidence that the aging brain loses the neurophysiological ability tocoordinate the two dominant oscillations of NREM sleep, in starkcontrast to the precise spatiotemporal coupling expressed in youngadults. However, these data alone do not necessarily establish that thisdynamic coupling profile is of functional benefit in young adults, andwhether coupling impairments in older adults is detrimental for olderadults. This issue is addressed next.

SO-Spindle Coupling and Overnight Memory Consolidation:

System consolidation theory suggests that new memories are transientlymore dependent on the hippocampus and then gradually transform to becomemore prefrontal-dependent. Endogenous NREM oscillatory activity isthought to provide the key functional substrate of timed informationtransfer between cortical regions. In particular, neocortical SO arethought to orchestrate thalamo-cortical sleep spindle and hippocampalripple activity during NREM sleep to facilitate the information transferbetween neocortical and hippocampal circuits. This nesting of multiplefrequency bands constitutes an oscillatory hierarchy, providing theprecise intrinsically-generated timing to route information from thehippocampus to neocortical areas at times of high excitability, which inturn facilitate long-term storage.

Several studies have inferred a role of the phase of the SO indetermining the success of memory consolidation using indirect measures.In addition, brain stimulation studies have established that entrainmentof SO can have reciprocal effects on sleep spindles, and vice versa.Most recently, it has been suggested that shifts in the exact SO-spindletiming could give rise to the behavioral benefits of electricalstimulation observed in rodents, which may also be beneficial incognitively impaired older individuals. Our results provide the firstdirect evidence that the exact timing of SO and sleep spindles in thehealthy, human brain significantly predicts the success of overnighthippocampus-dependent memory retention.

A further novel discovery of the current study is the demonstration thatthe precise temporal interplay of SO and sleep spindles is disrupted inolder adults, wherein sleep spindles were misaligned (often occurringtoo early in the SO cycle) relative to the precise timing in youngadults. The process of human brain aging appears to weaken the otherwiserobust NREM oscillatory hierarchy, reducing the optimal SO-spindle phasetiming, and in doing so, predicts impaired memory consolidation. Twopoints are of relevance in this regard. First, the finding that thememory-SO-spindle coupling relationship was significant in older adults,but at an earlier phase, importantly demonstrates that both SO and sleepspindles were still expressed in older adults. However, the mechanismthat couples them in time is impaired, with spindles systematicallyarriving too early within the SO cycle. Second, it establishes that thisimpaired SO-spindle temporal coupling diminished the magnitude ofovernight memory consolidation benefit.

Neurophysiological Correlates of Age-Related Memory Decline:

A multitude of studies demonstrated that aging affects sleeparchitecture and memory. However, no neurophysiological mechanism hasbeen identified that functionally links age-related changes in sleepphysiology to impaired memory retention, beyond quantitative reductionsin the amount of oscillatory activity. Here, evidence is provided thataddresses this mechanistic gap in understanding, demonstrating the lossof temporal SO-spindle coupling over specific topographical regionsexplains the degree of failed of memory consolidation.

Interestingly, when only focusing on the basic phase measure, sleepspindles lock as precisely to the rising flank of the SO in older adultsas they do young adults, as shown in FIGS. 3C and 3D. However, ourdirectional phase analyses revealed the existence of a clear age-relateddeficit. Specifically, inspection of SO-spindle interactions, shown inFIG. 3G, demonstrated that the directional influence of SO on sleepspindle timing was diminished in older adults relative to young adults,resulting in a misaligned arrival of the spindle relative to the SO.Moreover, this age-related impairment was found to be not equivalentacross all brain regions, but was expressed most significantly overprefrontal cortex sensors and less strongly over parieto-occipitalregions (FIG. 3G). These results further establish that, in later adultlife, the human adult brain experiences a decline in the ability toprecisely coordinate SO with cortico-thalamic sleep spindles.Specifically, spindles are more often expressed at unfavorable SO phasesin older adults, arriving too early to confer optimalhippocampus-dependent memory consolidation benefits.

One testable hypothesis in animal models emerging from our findings isthat the impaired coordination of temporal SO-spindle coupling overprefrontal cortex does not trigger hippocampal ripples as effectively,and that the magnitude of that failure should predict the consequentialdegree of impaired rather than successful sleep-dependent memoryconsolidation.

Prefrontal Atrophy, SO-Spindle Coupling and Aging:

Beyond impairments in memory-relevant SO-spindle coupling in olderrelative to young adults, it was further established that at least onepathological alteration contributing to the severity of this age-relatedcoupling dysfunction—grey matter atrophy within the mPFC. Grey mattervolume of the medial prefrontal cortex predicts inter-individualdifferences in the quality of SO in both young and older adults.Moreover, high-density EEG evidence has defined a role for the mPFC inthe generation of slow waves.

Our findings advance this anatomical-neurophysiological connection. Itwas shown that the decrease in structural integrity of the mPFC accountsfor the qualitative degree of impaired temporal phase coupling betweenthe SO and sleep spindles. Thus, mPFC atrophy, in addition to reducingSO incidence and intensity, contributes to misalignment of the timing ofsleep spindles relative to the SO phase. This effect is greatest overfrontal EEG derivations, indicating that the mPFC may play aparticularly important role in the regulation of the coordinated timingof NREM sleep oscillations

The results reveal that the structural integrity of mPFC is one factordetermining the capacity of the human brain to precisely and optimallycoordinate the timed arrival of sleep spindles with the SO, and in doingso, dictate the success or failure of hippocampus-dependent memoryconsolidation. Intracranial studies taking advantage of invasiverecordings from multiple regions of interest in concert, such as mPFC,the hippocampus, and thalamus will help to clarify the directionalinfluences of cortical-subcortical interaction not possible with scalpEEG recordings. Such recordings occurring combined with sleep-dependentmemory tasks would further our current understanding of the coordinatedinteractions between the mPFC, thalamus, and hippocampus that occursduring NREM sleep oscillations, as well as their necessity andsufficiency in supporting long-term memory retention.

Our findings reveal a fundamental neurophysiologic mechanism involvingthe spatiotemporal coupling between the SO and the sleep spindle, anddemonstrate that this temporal synchrony is functionally andbehaviorally relevant for the success of overnight memory consolidation.It was further shows that this same neurophysiological oscillatorydynamic is impaired in older relative to young adults, leading toimprecise sleep spindle expression in relationship with the depolarizing‘up-state’ of the SO. Moreover, our findings reveal that age-relatedprefrontal gray matter atrophy represents one neuropathologicalsubstrate explaining the attenuation of this oscillatory controlmechanism, which thus impairs hippocampus-dependent memoryconsolidation.

Our results are of potential clinical relevance in two ways. First, theydocument the presence of an under-appreciated pathway—impaired temporalprecision of sleep oscillation coupling—that contributes to memorydecline in later life. Second, they help define sleep oscillatorysynchrony as a novel therapeutic target for modulation ofhippocampus-dependent memory consolidation in older adults, andpotentially in those with mild cognitive impairment and Alzheimer'sdisease. This may be achieved using non-invasive entrainment by means ofacoustic, electric, or magnetic brain stimulation, aiming to restore thetemporally precise SO-spindle coordination closer to that of youngadults, helping reduced the impact of cognitive decline in aging.

Additional details are provided regarding related experimental modelsand subjects.

Participants:

32 healthy older (mean age: 73.7±5.3; mean±SD) and 20 younger adults(20.4±2.0 years) participated in the study. All participants providedwritten informed consent according to the local ethics committee(Berkeley Committee for Protection of Human Subjects Protocol Number2010-01-595) and the Declaration of Helsinki. Data from a subset ofparticipants has been reported previously.

Experimental Design and Procedure:

All participants were trained on the episodic word-pair task in theevening and performed a short recognition test after 10 min. Then,participants were offered an 8 h sleep opportunity, starting at theirhabitual bedtime. Polysomnography was collected continuously.Participants performed a long version of the recognition testapproximately 2 h after awakening. Subsequently, structural MRI scanswere obtained from all participants. Two older adults did not completebehavioral testing, and two young adults failed to achieve criterion atencoding. Thus, these four subjects were excluded from behavioralanalyses, but were included in all electrophysiological and imaginganalyses.

Behavioral Task:

A previously established sleep-dependent episodic memory task (FIG. 2A)was utilized, where subjects had to learn word-nonsense word pairs. Inbrief, words were 3-8 letters in length and drawn from a normative setof English words, while nonsense words were 6-14 letters in length andderived from groups of common phonemes. During encoding, subjectslearned 120 word-nonsense pairs. Each pair was presented for 5 s.Participants performed the criterion training immediately afterencoding. The word was presented along with the previously learnednonsense word and two new nonsense words. Subjects had to choose thecorrectly associated nonsense words and received feedback afterwards.Incorrect trials were repeated after a variable interval, and werepresented with two additional new nonsense words to avoid repetition ofincorrect nonsense words. Criterion training continued until correctresponses were observed for all trials.

During recognition, a probe word or a new (foil) probe word waspresented along with 4 options: (1) the originally paired nonsense word,(2) a previously displayed nonsense word, which was linked to adifferent probe (lure), (3) a new nonsense word or (4) an option toindicate that the probe is new. During the recognition test after ashort delay (10 min), 30 probe and 15 foil trials were presented. At thelong delay (10 h), 90 probe and 45 foil trials were tested. All probewords were presented only once during recognition testing, either duringshort or long delay testing

Sleep Monitoring and EEG Data Acquisition:

Polysomnography (PSG) sleep monitoring was recorded on a GrassTechnologies Comet XL system (Astro-Med), including 19-channelelectroencephalography (EEG) placed using the standard 10-20 system aswell as Electromyography (EMG). Electrooculogram (EOG) was recorded theright and left outer canthi. EEG recordings were referenced to bilaterallinked mastoids and digitized at 400 Hz in the range from 0.1-100 Hz.Sleep scoring was performed according to standard criteria in 30 sepochs. Slow wave sleep (SWS) was defined as NREM stages 3-4, while NREMsleep encompassed stages 2-4. Given that stage 2 does not always exhibitpronounced SO activity (FIGS. 2C and 2D), the study focused on SWS forall correlational analyses.

MRI Data Acquisition:

Scanning was performed on a Siemens Trio 3T scanner with a 32-channelhead coil. Two high-resolution T1-weighted anatomical images wereobtained, which were acquired using a three-dimensional MPRAGE protocolwith the following parameters: repetition time, 1900 ms; echo time, 2.52ms; flip angle, 9°; field of view, 256 mm; matrix, 256×256; slicethickness, 1.0 mm; 176 slices. MPRAGE images were co-registered, and themean image was used to perform optimized voxel-based morphometry (VBM)to examine grey matter volume within specified regions of interest (ROI)as described below.

Additional details are provided regarding quantification and statisticalanalysis.

Behavioral Data Analysis:

Memory recognition was calculated by subtracting both the false alarmrate (proportion of foil words, which subjects' reported as previouslyencountered) and the lure rate (proportion of words that were pairedwith a familiar, but incorrect nonsense word) from the hit rate(correctly paired word-nonsense word pairs). Memory retention wassubsequently calculated as the difference between recognition at longminus short delays.

EEG Data:

Preprocessing: EEG data were imported into EEGLAB and epoched into 5 sbins, which were visually inspected for artifacts. Then the continuousdata was exported to FieldTrip for further analyses.

Spectral analysis: (1) To obtain the average power spectra (FIG. 2B),the raw data was epoched into non-overlapping 15 second segments andepochs containing artifacts were rejected. Data was tapered with aHanning window and spectral estimates were calculated from 0.5 to 50 Hzin 0.5 Hz steps and averaged per subject and channel for all epochs inNREM sleep. (2) To obtain a continuous time-frequency representation ofa whole night of sleep (FIGS. 2C and 2D), multitaper spectral analyseswere utilized, based on discrete prolate slepian sequences. The raw datawas epoched into 30 second long segments, with 85% overlap. Spectralestimates were obtained between 0.5 and 30 Hz in 0.5 Hz steps. 29 taperswere utilized, providing a frequency smoothing of ±0.5 Hz.

Event detection: Event detection (FIG. 2D and FIGS. 3A and 3C) wasperformed for every channel separately based on previously establishedalgorithms. (1) Slow oscillations: In brief, the continuous signalbetween 0.16 and 1.25 Hz were first filtered and all the zero crossingswere detected. Then events were selected based on time (0.8-2 sduration) and amplitude (75% percentile) criteria. Finally,artifact-free 5 s long segments (±2.5 s around trough) were extractedfrom the raw signal. (2) Sleep spindles: The signal between 12-16 Hz wasfiltered and the analytical amplitude was extracted after applying aHilbert transform. The amplitude was smoothed with a 200 ms movingaverage. Then the amplitude was thresholded at the 75% percentile(amplitude criterion) and only events that exceeded the threshold for0.5 to 3 s (time criterion) were accepted. Artifact-free events werethen defined as 5 s long sleep-spindle epochs (±2.5 s), peak-locked.Given that prominent power differences were observed between young andolder adults (FIG. 2B), events per subjects were normalized by means ofa z-score prior to all subsequent analyses, unless stated otherwise(FIG. 3A). The mean and standard deviation were derived from theunfiltered event-locked average time course of either SO or spindleevents (e.g. FIGS. 2C and 2D; lower right) in every participants.Z-scores were then computed for all trials and time points.

Event-locked spectral analysis: Time-frequency representations forartifact-free normalized SO (FIG. 3B) were calculated after applying a500 ms Hanning window. Spectral estimates (0.5-30 Hz; 0.5 Hz steps) werecalculated between −2 and 2 s in steps of 50 ms and baseline-correctedby means of z-score relative to a bootstrapped baseline distributionthat was created from all trials (baseline epoch −2 to −1.5 s, 10000iterations).

Event-locked cross-frequency coupling: For event-locked cross-frequencyanalyses, we first filtered the normalized SO trough-locked data (FIGS.3D and 3E; spindle-locked in FIGS. 2C and 2D) into the SO component(0.1-1.25 Hz) and extracted the instantaneous phase angle after applyinga Hilbert transform. Then we filtered the same trials between 12-16 Hzand extracted the instantaneous amplitude from the Hilbert transform. Weonly considered the time range from −2 to 2 s to avoid filter edgeartifacts. For every subject, channel, and epoch, we now detected themaximal sleep spindle amplitude and corresponding SO phase angle. Themean circular direction and resultant vector length across all NREMevents were determined using the CircStat toolbox. In addition, wedivided the SO phase into 17 linearly spaced bins and calculated themean sleep spindle amplitude per bin. We normalized the individual sleepspindle amplitude distribution by the mean across all bins.

Data-driven cross-frequency coupling: We calculated a comodulogram on15-second artifact-free long non-overlapping z-normalized segmentsduring NREM sleep. We calculated the modulation index between lower(0.5-6.5 Hz; 0.5 Hz steps) and faster frequencies (8-40 Hz; 1 Hz steps).For the low frequency, we utilized a window of ±1 Hz, which was adjustedfor the lowest frequencies. For faster frequencies, the window wasadjusted to capture the side peaks. Hence, the window at a givenfrequency was always defined as the low center frequency+1 Hz. I.e. at15 Hz, the window to assess coupling to the 3 Hz phase was ±4 Hz; whileat 5 Hz the window was ±6 Hz. The modulation index was normalized by abootstrapped z-score relative to a distribution that was obtained byrandom-point block-swapping (200 iterations).

Cross-Frequency Directionality Analysis:

To determine whether low frequencies components drive sleep spindleactivity during SWS or vice versa, we calculated the cross-frequencyphase slope index between the normalized signal and the signal filteredin the sleep spindle range (12-16 Hz). To avoid edge artifacts, werestricted this analysis to ±2 seconds around the SO trough. Hence,these 4 second long segments include at least 3 cycles of the SOoscillation (˜0.75 Hz), in accordance with previous reports. Weconsidered frequencies between 0.5 and 4 Hz (0.5 Hz steps; 0.25 Hzbandwidth) after applying a Hanning window and extracting the complexFourier coefficients. Significant values above zero indicate that SOdrive sleep spindle activity, while negative values indicate that sleepspindles drive SO. Values around zero indicate no directional coupling.We repeated this analysis based on 15 second long segments, which werethen averaged across all available NREM events to demonstrate that thefindings are not confounded by the chosen window length.

Detection of SO and Spindle Frequency Peaks:

(1) SO peak frequency (related to FIG. 2B): In order to disentangle thetrue oscillatory SO component from the prominent 1/f slope, we utilizedirregular-resampling auto-spectral analysis (IRASA; Wen and Liu, 2016).We analyzed non-overlapping 15 s segments of continuous artifact-freedata during NREM sleep and assessed frequencies between 0.1 and 30 Hz.IRASA takes advantage of the fact that irregularly resampling of theneuronal signals by pairwise non-integer values (resampling factor rfand corresponding factor rf′: e.g. 1.1 and 0.9) slightly shifts the peakfrequency of oscillatory signals by compressing or stretching theunderlying signal. However, the 1/f component remains stable. Thisprocedure is then repeated in small, overlapping windows (window size: 5s, sliding steps: 1 s; resampling factors rf: 1.1-1.9 in 0.05increments). Note resampling was always done in a pairwise fashion forfactor h and the corresponding resampling factor rf*=2−rf. For eachsegment, we calculated the auto-power spectrum by means of a FFT afterapplying a Hanning window. Then all auto-spectra were median-averaged toobtain the power spectrum of the 1/f component, with the idea being thatresampled oscillatory components are averaged out. Finally, theresampled 1/f PSD is subtracted from the original PSD to obtain theoscillatory residuals on which we performed the individual peakdetection (SO range: peak <2 Hz; spindle-range: 9-17 Hz).

(2) In addition to IRASA, which provides a mean sleep spindle peakfrequency, we also utilized a linear de-trending approach to assessspindle frequencies as a function of the SO phase (FIG. 4C), where weinvestigated whether SO modulates additional sleep spindle featuresbesides the amplitude on a fine-grained temporal scale. Therefore, wescreened every artifact-free normalized SO event (−1.25 to 1.25 aroundtrough) at every channel separately for oscillatory activity in thesleep spindle range. First, we zero-padded every trial to 10 seconds toincrease the frequency resolution (0.1 Hz), then applied a Hanningwindow and obtained spectral estimates between 8 and 16 Hz. Theresulting power values were log transformed. The sleep spindle peak forevery SO was detected after subtraction of linear fit to the spectrum toremove the 1/f component. Second, every trial was filtered at thetrial-specific peak frequency (±2 Hz) and the instantaneous amplitudewas extracted from a Hilbert transform before we performed event-relatedcross-frequency coupling analyses. In addition, we only considered SOthat contained sleep spindle events that exceeded the 75% percentile ofsleep spindle amplitudes to ensure comparability for correlationanalyses. This approach effectively corrected for differences in sleepspindle peak frequencies and spectral power distributions prior tocorrelation with behavior. To obtain time-resolved sleep spindle peakfrequency estimates, the sleep spindle peak was detected as describedabove in a 500 ms sliding window approach. The window in 25 ms steps wasshifted relative to the SO events (−1 to 1 s; ±250 ms) and recalculatedthe sleep spindle peak frequency. Finally, the resulting traces weresmoothed with 100 ms moving average.

Structural MRI Data Analysis:

To measure grey matter volume, optimized voxel-based morphometry (VBM)was performed using SPM8 with the VBM8 toolbox and the DiffeomorphicAnatomical Registration through Exponentiated Lie algebra (DARTEL)toolbox in order to improve registration of older brains to thenormalized MNI template. To enhance signal to noise ratio, twoT1-weighted MPRAGE images were first co-registered and averaged.Averaged images were then segmented applying the Markov random fieldapproach and then registered, normalized, and modulated using DARTEL.Grey matter and white matter segmentations were inputted into DARTEL andutilized to create a study specific template, which was then used tonormalize individual brains into MNI space. Modulated grey matter mapswere then smoothed using an 8 mm Gaussian kernel.

Measures of total intracranial volume (TIV) for each participant wereestimated from the sum of grey matter, white matter, and CSFsegmentation, and then used to adjust grey matter volumetric measures toaccount for differences in head size. Given that slow oscillations,sleep spindles, and ripples depend on the interaction between prefrontalcortex, thalamus, and hippocampus regions, the Anatomical AutomaticLabeling repository within the Wake Forest University PickAtlas toolboxwas used to generate anatomically-based ROIs for the hippocampus,thalamus, medial prefrontal cortex, and orbitofrontal cortex, as well asan occipital lobe control ROI. Mean voxelwise gray matter volume withinanatomically defined ROIs were extracted using the Marsbar toolbox andused in analyses relating grey matter volumetric measures with sleep andmemory variables.

Statistical Analysis:

Unless stated otherwise, cluster-based permutation tests were used tocorrect for multiple comparisons as implemented in FieldTrip (MonteCarlo method; 1000 iterations; maxsize criterion). Clusters were formedin time/frequency (e.g. FIGS. 3B and 3F) or space (e.g. FIGS. 3E and 3G)by thresholding independent t-tests (e.g. FIGS. 3E-G), circular-linear(e.g. FIGS. 4A and 4D) or linear correlations (Spearman, e.g. FIG. 5A)at p<0.05. Correlation values were transformed into t-values. Apermutation distribution was then created by randomly shuffling labels.The permutation p-value was obtained by comparing the cluster statisticto the random permutation distribution. The clusters were consideredsignificant at p<0.05 (two-sided). Bonferroni-correction was applied tocorrect for multiple cluster tests (e.g. FIG. 5B).

Circular statistics were calculated using the CircStat toolbox. Circularnon-uniformity was assessed with Rayleigh tests at p<0.01. Effect sizeswere quantified by means of Cohen's d, the correlation coefficient rhoor η2 in case of repeated measures ANOVAs or Watson-Williams-tests(circular ANOVA equivalent). Circular-linear correlations werecalculated according to the following equations 1-4:

$\begin{matrix}{\rho = \sqrt{\frac{r_{xs}^{2} + r_{xc}^{2} - {2*r_{xs}*r_{xc}*r_{cs}}}{1 - r_{cs}^{2}}}} & (1)\end{matrix}$

In various embodiments, r_(xs), r_(xc) and r_(cs) were defined as

r _(xs)=corr(x,sin(alpha))  (2)

r _(xc)=corr(x,cos(alpha))  (3)

r _(cs)=corr(sin(alpha),cos(alpha))  (4)

Where x is the linear and alpha being the circular variable. In order tocontrol for confounding variables, partial correlations were utilized,where c was partialled out of x, sin(alpha) and cos(alpha) beforecomputing the multiple correlation using the regression residuals. Athreshold of 10% was utilized to define clusters following partialcorrelations, which were then again tested at a cluster alpha of 0.05.To obtain effect sizes for cluster tests, the effect size separately forall channel, frequency and/or time points was calculated and averagedacross all data points in the cluster. Repeated-measures ANOVAs wereGreenhouse-Geisser corrected.

Additional details are provided regarding FIGS. 6-11, which illustratebrain stimulation used to enhance sleep and facilitate awakening. Asshown in FIGS. 6-11, N=8. As illustrated, stimulation has an effect onNREM slow activity. In various embodiments, stimulation appears to betriggering a state-specific EEG effect. Whatever the dominant frequencyof the state that is observed (sleep, or just Stage 2, or just SWS, orjust REM), stimulation seems to selectively increase the innate dominaterhythm of that brain state. In addition, effects are visible on grosssleep amounts in terms of time. Also visible are about a 6-8% increasein NREM SWS %. FIGS. 7-11 illustrate t stat mapsisitic differencesbetween Stim and Sham in spectral activity bins during the PSG nap, withred colors indicating positive increases due to Stim, and blue colorsindicating negative decreases due to Stim.

With regard to FIG. 7, illustrated are the Stim vs Sham differences forall sleep, combined (i.e., all stages combined). Note that Stim isworking, such that Anodal stim before sleep is subsequently increasingthe amount of Ultra slow Delta (<1 Hz) wave power, as well as generalDelta (0.8-4 Hz). Also note that Stim is decreasing the overall amountof faster frequency activity, considered to be “non-restorative” sleepin terms of EEG.

With regard to FIG. 8, illustrated are the Stim vs Sham differences forall of NREM sleep (i.e., Stage 1-4, combined). Similar to above, Stim isworking such that Anodal stim before sleep is subsequently increasingthe amount of Ultra slow Delta (<1 Hz) wave power, and especiallygeneral Delta (0.8-4 Hz). Also note that Stim is decreasing the overallamount of faster frequency activity, considered to be “non-restorative”sleep in terms of EEG.

With regard to FIG. 9, illustrated are the Stim vs Sham differences forjust NREM stage 2, and somewhat different to NREM or SWS. The strongereffects seen are actually for sigma activity—which is the sleep spindlerange, consistent with spindles be predominantly a stage-2 phenomenon.Weaker effects are visible on decreasing overall amount of fasterfrequency activity.

With regard to FIG. 10, illustrated are the Stim vs Sham differences forNREM and SWS (stages 3&4). Similar to above, but weaker effects arevisible in slow and general delta range. Also note that Stim isdecreasing the overall amount of faster frequency activity, consideredto be “non-restorative” sleep in terms of EEG.

With regard to FIG. 11, illustrated are the Stim vs Sham differences forREM sleep. First, the t stats appear to be large, and aspects of theelectrodes may be driving this. Also noted in FIG. 11 is that Stim seemsto be increasing theta activity, which is the dominant spectral rhythmof REM sleep.

With reference to FIG. 12, shown is a flow chart of a method 1200 forproviding brain monitoring and stimulation, implemented in accordancewith some embodiments. As discussed above, various components of system100 may be configured to implement modeling and closed loop managementof treatments and therapies provided to a user.

Accordingly, method 1200 may commence with operation 1202 during whichmeasurements are obtained from a brain via an interface. Themeasurements may represent neural activity over a particular period oftime, or temporal window, and may be obtained via components of a braininterface. Such measurements may be acquired and stored in a memory. Invarious embodiments, the plurality of measurements includes indicationsof slow wave oscillations and sleep spindles as described above.

Method 1200 may proceed to operation 1204 during which parametersassociated with brain states are generated. As similarly discussedabove, such parameters may include observers and estimators associatedwith brain states as well as identification of the brain statesthemselves. Such brain states may include an unconscious statecorresponding to NREM sleep phases. Such parameters may be generated bya first processing device and may be stored in a local memory. Inparticular embodiments, the brain state parameters include an indicationof a synchrony pattern between the measured slow wave oscillations andthe measured sleep spindles, as described above. In some embodiments,the generation of brain state parameters may include assessment of thetemporal directionality of SO-spindle interactions.

Method 1200 may proceed to operation 1206 during which functional andstructural models are generated based, at least in part, on themeasurements and the parameters. As discussed above, such models mayemulate functions, tasks, and components of the user's brain, and may beconfigured based on the brain's activity and behavior. Such models mayalso be configured based on previously obtained reference data. Suchmodels may also be configured based on the determined synchrony patternof measured slow wave oscillations and the measured sleep spindles.

Method 1200 may process to operation 1208 during which a procedure formediation is determined. In some embodiments, the procedure formediation is configured to adjust the intensity of slow waveoscillations of the subject. Such adjustments, such as increasing theslow wave oscillation intensity, may cause enhanced slow waveoscillation and spindle synchrony. In some embodiments, the models ofthe brain are used to determine the procedure for mediation. The modelsof the brain may include predicted timing of the peaks and valleys ofvarious brain waves of the subject's brain, which may be used todetermine the timing for applying mediating stimuli to such brain

In some embodiments, training data is additionally used, with thetraining data consisting of one or more mediation data points. In someembodiments, the mediation data points include, for example, one or moremodels of additional users' brains, or one or more previous proceduresfor stimulation of brain wave oscillations. In some embodiments, thetraining data is used in conjunction with machine learning algorithms orartificial intelligence modalities. In some embodiments, the machinelearning algorithms or artificial intelligence modalities determine aprocedure for mediation based on one or more data points within thetraining data that are determined to suggest a procedure for mediationthat adjusts intensity, frequency, or power of various brain waves. Forexample, if one or more data points of previous mediation of brain wavesof users suggest, via machine learning algorithms that process andanalyze the data points, a method that leads to optimal increase in slowwave oscillation intensity, then a procedure for mediation of the user'sbrain waves can be determined based on those data points.

In some embodiments, method 1200 may proceed to operation 1209 duringwhich the procedure for mediation is provided to one or more entities.In some embodiments, the one or more entities may be, for example,client devices, user devices 110, first processing device 104, secondprocessing device 106, controller 108, one or more external applicationsor websites, or some combination thereof.

Method 1200 may proceed to operation 1210 during which a control signalis generated based, at least in part on, on the measurements and themodels. Accordingly, one or more control signals may be generated basedon recent neural activity represented by measurement data, and alsobased on expected or desired effects as determined based on the models.In this way, specific control signals may be generated to implement aparticular cognitive modulation that is specifically configured for theuser. Moreover, as discussed above and in greater detail below, suchcontrol signals may be generated and implemented in a closed loopmanner.

Method 1200 may proceed to operation 1212 during which the controlsignal is provided to the interface. Accordingly, the control signal maybe provided to the interface which may generate one or more stimulibased on the control signal. For example, such stimuli may includeelectrical stimuli, visual stimuli, aural stimuli, and/or tactilestimuli that may have parameters, such as amplitude and duration,determined based on the control signal. Such stimuli, such as electricalstimuli, may be applied to the cortical tissue of the brain.

Method 1200 may proceed to operation 1212 during which it may bedetermined if additional measurements should be made. In variousembodiments, such a determination may be made based on a current state,or in response to one or more conditions. For example, if a particulartherapeutic regimen is implemented, a series of measurement may be madeaccording to a predetermined schedule, and such measurements may bestepped through utilizing a state machine. If it is determined thatadditional measurements should be made, method 1200 may return tooperation 1202. If it is determined that no additional measurementsshould be made, method 1200 may terminate.

FIG. 13 illustrates an example of a computer system or a processingdevice 1300 that can be used with various embodiments. For example,processing device 1300 can be used to implement interface 102, firstprocessing device 104, second processing device 106, controller 108,client device 110, and/or prosthetic 112 according to variousembodiments described above. For instance, the processing device 1302can be used to implement brain stimulation in accordance with thevarious embodiments described above. In addition, the processing device1302 shown can represent a computing system on a mobile device or on acomputer or laptop, etc. According to particular example embodiments, aprocessing device 1302 suitable for implementing particular embodimentsof the present invention includes a processor 1301, a memory 1303, aninterface 1311, and a bus 1315 (e.g., a PCI bus). The interface 1311 mayinclude separate input and output interfaces, or may be a unifiedinterface supporting both operations. When acting under the control ofappropriate software or firmware, the processor 1301 is responsible fortasks such as brain stimulation described above. Various speciallyconfigured devices can also be used in place of a processor 1301 or inaddition to processor 1301. The complete implementation can also be donein custom hardware. The interface 1311 is typically configured to sendand receive data packets or data segments over a network. Particularexamples of interfaces the device supports include Ethernet interfaces,frame relay interfaces, cable interfaces, DSL interfaces, token ringinterfaces, and the like.

In addition, various very high-speed interfaces may be provided such asfast Ethernet interfaces, Gigabit Ethernet interfaces, ATM interfaces,HSSI interfaces, POS interfaces, FDDI interfaces and the like.Generally, these interfaces may include ports appropriate forcommunication with the appropriate media. In some cases, they may alsoinclude an independent processor and, in some instances, volatile RAM.The independent processors may control such communications intensivetasks as packet switching, media control and management.

According to particular example embodiments, the processing device 1302uses memory 1303 to store data and program instructions and maintain alocal side cache. The program instructions may control the operation ofan operating system and/or one or more applications, for example. Thememory or memories may also be configured to store received metadata andbatch requested metadata.

Because such information and program instructions may be employed toimplement the systems/methods described herein, the present inventionrelates to tangible, machine readable media that include programinstructions, state information, etc. for performing various operationsdescribed herein. Examples of machine-readable media include hard disks,floppy disks, magnetic tape, optical media such as CD-ROM disks andDVDs; magneto-optical media such as optical disks, and hardware devicesthat are specially configured to store and perform program instructions,such as read-only memory devices (ROM) and programmable read-only memorydevices (PROMs). Examples of program instructions include both machinecode, such as produced by a compiler, and files containing higher levelcode that may be executed by the computer using an interpreter.

While the present disclosure has been particularly shown and describedwith reference to specific embodiments thereof, it will be understood bythose skilled in the art that changes in the form and details of thedisclosed embodiments may be made without departing from the spirit orscope of the invention. Specifically, there are many alternative ways ofimplementing the processes, systems, and apparatuses described. It istherefore intended that the invention be interpreted to include allvariations and equivalents that fall within the true spirit and scope ofthe present invention. Moreover, although particular features have beendescribed as part of each example, any combination of these features oradditions of other features are intended to be included within the scopeof this disclosure. Accordingly, the embodiments described herein are tobe considered as illustrative and not restrictive.

What is claimed is:
 1. A method comprising: obtaining a plurality ofmeasurements from a brain of a user via an interface, wherein theplurality of measurements includes indications of slow wave oscillationsand sleep spindles; generating, via a first processing device comprisingone or more processors, a plurality of brain state parameterscharacterizing one or more features of at least one brain state of thebrain of the user, wherein the brain state parameters include anindication of a synchrony pattern between the measured slow waveoscillations and the measured sleep spindles; via a second processingdevice comprising one or more processors, generating a model of thebrain of the user based, at least in part, on the plurality ofmeasurements and the synchrony pattern; and determining, using the modelof the brain of the user and training data comprising one or moremediation data points, a procedure for mediation configured to adjustthe intensity of slow wave oscillations; and generating one or morecontrol signals, via a controller, based on the procedure for mediation,wherein the one or more control signals are transmitted to theinterface.
 2. The method of claim 1 further comprising: generating, viathe interface, one or more stimuli based on the one or more controlsignals, wherein the one or more stimuli is configured to increase slowwave oscillation power of the brain; and applying the one or morestimuli to cortical tissue of the brain.
 3. The method of claim 2,wherein the one or more stimuli is configured to enhance slow waveoscillation and spindle synchrony.
 4. The method of claim 1, wherein theat least one brain state includes an unconscious state corresponding tonon-rapid-eye-movement sleep.
 5. The method of claim 1, whereingenerating a plurality of brain state parameters includes assessing atemporal directionality of interactions between the measured slow waveoscillations and sleep spindles.
 6. The method of claim 1, furthercomprising providing the procedure for mediation to one or moreentities.
 7. The method of claim 6, wherein the one or more entitiesincludes a client device corresponding to a medical professional.
 8. Asystem comprising: an interface configured to obtain a plurality ofmeasurements from a brain of a user, wherein the plurality ofmeasurements includes indications of slow wave oscillations and sleepspindles; a first processing device comprising one or more processorsconfigured to generate a plurality of brain state parameterscharacterizing one or more features of at least one brain state of thebrain of the user, wherein the brain state parameters include anindication of a synchrony pattern between the measured slow waveoscillations and the measured sleep spindles; a second processing devicecomprising one or more processors configured to: generate a model of thebrain of the user based, at least in part, on the plurality ofmeasurements and the synchrony pattern; and determine, using the modelof the brain of the user and training data comprising one or moremediation data points, a procedure for mediation configured to adjustthe intensity of slow wave oscillations; and a controller comprising oneor more processors configured to generate one or more control signalsbased on the procedure for mediation, wherein the one or more controlsignals are transmitted to the interface.
 9. The system of claim 8,wherein the interface is further configured to: generate one or morestimuli based on the one or more control signals, wherein the one ormore stimuli is configured to increase slow wave oscillation power ofthe brain; and apply the one or more stimuli to cortical tissue of thebrain.
 10. The system of claim 9, wherein the one or more stimuli isconfigured to enhance slow wave oscillation and spindle synchrony. 11.The system of claim 8, wherein the at least one brain state includes anunconscious state corresponding to non-rapid-eye-movement sleep.
 12. Thesystem of claim 8, wherein generating a plurality of brain stateparameters includes assessing a temporal directionality of interactionsbetween the measured slow wave oscillations and sleep spindles.
 13. Thesystem of claim 8, wherein the second processing device is furtherconfigured to provide the procedure for mediation to one or moreentities.
 14. The system of claim 13, wherein the one or more entitiesincludes a client device corresponding to a medical professional.
 15. Anon-transitory computer readable medium storing one or more programsconfigured for execution by a computer, the one or more programscomprising instructions for: obtaining a plurality of measurements froma brain of a user via an interface, wherein the plurality ofmeasurements includes indications of slow wave oscillations and sleepspindles; generating, via a first processing device comprising one ormore processors, a plurality of brain state parameters characterizingone or more features of at least one brain state of the brain of theuser, wherein the brain state parameters include an indication of asynchrony pattern between the measured slow wave oscillations and themeasured sleep spindles; via a second processing device comprising oneor more processors, generating a model of the brain of the user based,at least in part, on the plurality of measurements and the synchronypattern; and determining, using the model of the brain of the user andtraining data comprising one or more mediation data points, a procedurefor mediation configured to adjust the intensity of slow waveoscillations; and generating one or more control signals, via acontroller, based on the procedure for mediation, wherein the one ormore control signals are transmitted to the interface.
 16. Thenon-transitory computer readable medium of claim 15, wherein the one ormore programs further comprise instructions for: generating, via theinterface, one or more stimuli based on the one or more control signals,wherein the one or more stimuli is configured to increase slow waveoscillation power of the brain; and applying the one or more stimuli tocortical tissue of the brain.
 17. The non-transitory computer readablemedium of claim 16, wherein the one or more stimuli is configured toenhance slow wave oscillation and spindle synchrony.
 18. Thenon-transitory computer readable medium of claim 15, wherein the atleast one brain state includes an unconscious state corresponding tonon-rapid-eye-movement sleep.
 19. The non-transitory computer readablemedium of claim 15, wherein generating a plurality of brain stateparameters includes assessing a temporal directionality of interactionsbetween the measured slow wave oscillations and sleep spindles.
 20. Thenon-transitory computer readable medium of claim 15, wherein the one ormore programs further comprise instructions for: providing the procedurefor mediation to one or more entities.